




"""simple docstring"""




import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''





				def __init__(			self						,					_a						,					_a						,					_a					):





							"""simple docstring"""
							super().__init__()
							self.register_modules(vqvae=_a						,					unet=_a						,					scheduler=_a					)





				@torch.no_grad()
				def __call__(			self						,					_a = 1						,					_a = None						,					_a = 0.0						,					_a = 50						,					_a = "pil"						,					_a = True						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   randn_tensor(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=_a						,					)
							lowerCamelCase      =   latents.to(self.device					)

							# scale the initial noise by the standard deviation required by the scheduler
							lowerCamelCase      =   latents * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(_a					)

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							lowerCamelCase      =   """eta""" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)

							lowerCamelCase      =   {}
							if accepts_eta:
										lowerCamelCase      =   eta

							for t in self.progress_bar(self.scheduler.timesteps					):
										lowerCamelCase      =   self.scheduler.scale_model_input(_a						,					_a					)
										# predict the noise residual
										lowerCamelCase      =   self.unet(_a						,					_a					).sample
										# compute the previous noisy sample x_t -> x_t-1
										lowerCamelCase      =   self.scheduler.step(_a						,					_a						,					_a						,					**_a					).prev_sample

							# decode the image latents with the VAE
							lowerCamelCase      =   self.vqvae.decode(_a					).sample

							lowerCamelCase      =   (image / 2 + 0.5).clamp(0						,					1					)
							lowerCamelCase      =   image.cpu().permute(0						,					2						,					3						,					1					).numpy()
							if output_type == "pil":
										lowerCamelCase      =   self.numpy_to_pil(_a					)

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=_a					)









"""simple docstring"""




def        a__					(							)		->   int:
			return 1



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else two_pence(x - 2							) + one_pence()



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else five_pence(x - 5							) + two_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else ten_pence(x - 10							) + five_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else twenty_pence(x - 20							) + ten_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else fifty_pence(x - 50							) + twenty_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else one_pound(x - 1_00							) + fifty_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else two_pound(x - 2_00							) + one_pound(snake_case__							)



def        a__					(							snake_case__ = 2_00							)		->   int:
			return two_pound(snake_case__							)


if __name__ == "__main__":
					print(solution(int(input().strip())))



